Johannes Jan Struijk

Background: Risk assessment of patients with syncope does not consider a short PR interval despite its association with increased risk of atrial fibrillation and all‐cause mortality. This study aimed to explore the association between the PR interval and all‐cause mortality and recurrent syncope in patients admitted to the hospital with syncope. Methods: We included patients with a diagnosis of syncope and an ECG recorded within 24 hours of hospital admission from the Danish Nationwide Electrocardiogram Cohort and divided patients into short (200 ms). Patients with ECG abnormalities or comorbidities influencing the PR interval or outcomes were excluded. Results: A total of 52 038 patients were included. Adjusting for age, sex, and relevant covariates the highest hazard ratio (HR) was observed in patients with short PR interval with an HR of 1.50 (95% CI, 1.24–1.80, P P =0.3566). Adjusted 5‐year cumulative incidence of all‐cause mortality was 18% for short PR interval, 14% for normal PR interval, and 13% for long PR interval. Regarding recurrent syncope, a HR of 1.14 (95% CI, 1.09–1.20. P <0.001) was seen for long PR interval. Adjusted 5‐year cumulative incidence of recurrent syncope was 23% in patients with a long PR interval. Conclusion: In patients with syncope, a short PR interval was associated with higher risk of all‐cause mortality; however, a long PR interval was associated with increased rate of recurrent syncope.

Fetal phonocardiography is a well-known auscultation technique for evaluation of fetal health. However, murmurs that are synchronous with the maternal heartbeat can often be heard while listening to fetal heart sounds. Maternal placental murmurs (MPM) could be used to detect maternal cardiovascular and placental abnormalities, but the recorded MPMs are often contaminated by ambient interference and noise. Objective: The aim of this study was to compare noise reduction methods to reduce noise in the recorded MPMs. Approach: 1) Bandpass filtering (BPF), 2) a multichannel noise reduction (MCh) using either Wiener filter (WF), Least-mean-square or Independent component analysis, 3) a combination of BPF with wavelet transient reduction (WTR) and 4) a combination of MCh and WTR. The methods were tested on signals recorded with two microphone units placed on the abdomen of pregnant women with an electrocardiogram (ECG) recorded simultaneously. The performance was evaluated using coherence and heart cycle duration error (HCD_Error) as compared with the ECG. Results: The mean of the absolute HCD_Error was 32.7 ms for the BPF with all methods significantly lower (p < 0.05) than BPF. The lowest errors were obtained for WTR-WF where the HCD_Error ranged 16.68–17.72 ms for seven different filter orders. All methods had significantly different coherence measure compared with BPF (p < 0.05). The lowest coherence was reached with WTR-WF (filter order 640) where the mean value decreased from 0.50 for BPF to 0.03. Significance: These results show how noise reduction techniques such as WF combined with wavelet denoising can greatly enhance the quality of MPM recordings.

Aims: Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (risk factor-weighted clinical likelihood, RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models.Methods and results: Four studies with heart sound recordings from 2222 patients were pooled and distributed into two data sets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (P < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC 2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models was 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL.Conclusion: The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.

Objective. The aim of this study was to find spectral differences of diagnostic interest in heart sound recordings of patients with coronary artery disease (CAD) and healthy subjects. Approach. Heart sound recordings from three studies were pooled, and patients with clear diagnostic outcomes (positive: CAD and negative: Non-CAD) were selected for further analysis. Recordings from 1146 patients (191 CAD and 955 Non-CAD) were analyzed for spectral differences between the two groups using Welch’s spectral density estimate. Frequency spectra were estimated for systole and diastole segments, and time-frequency spectra were estimated for first (S1) and second (S2) heart sound segments. An ANCOVA model with terms for diagnosis, age, gender, and body mass index was used to evaluate statistical significance of the diagnosis term for each time-frequency component. Main results. Diastole and systole segments of CAD patients showed increased energy at frequencies 20–120 Hz; furthermore, this difference was statistically significant for the diastole. CAD patients showed decreased energy for the mid-S1 and mid-S2 segments and conversely increased energy before and after the valve sounds. Both S1 and S2 segments showed regions of statistically significant difference in the time-frequency spectra. Significance. Results from analysis of the diastole support findings of increased low-frequency energy from previous studies. Time-frequency components of S1 and S2 sounds showed that these two segments likely contain heretofore untapped information for risk assessment of CAD using phonocardiography; this should be considered in future works. Further development of features that build on these findings could lead to improved acoustic detection of CAD.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The purpose of this study was to investigate the correlation between the seismocardiogram and cardiorespiratory fitness. Cardiorespiratory fitness can be estimated as VO₂max using non-exercise algorithms, but the results can be inaccurate. Healthy subjects were recruited for this study. Seismocardiogram and electrocardiogram were recorded at rest. VO₂max was measured during a maximal effort cycle ergometer test. Amplitudes and timing intervals were extracted from the seismocardiogram and used in combination with demographic data in a non-exercise prediction model for VO₂max. 26 subjects were included, 17 females. Mean age: 38.3±9.1 years. The amplitude following the aortic valve closure derived from the seismocardiogram had a significant correlation of 0.80 (p2max. This feature combined with age, sex and BMI in the prediction model, yields a correlation to VO₂max of 0.90 (p−1·min⁻¹ . The seismocardiogram carries information about the cardiorespiratory fitness. When comparing to other non-exercise models the proposed model performs better, even after cross validation. The model is limited when tracking changes in VO₂max. The method could be used in the clinic for a more accurate estimation of VO₂max compared to current non-exercise methods.